Amartya Sen defined human development as the process of expanding the real freedoms that people enjoy. He focused on human freedoms which is a broader way of defining human development compared to more narrow views such as growth of GNP, growth of individual incomes, industrialization, and etc. Sen then goes on to describe that development requires the removal of unfreedom. Poverty is a source of unfreedom and it inhibits development through restricting the freedom of humans.
Poverty is a dilemma faced by humans across the globe, specifically it is a challenge toward Amartya Sen’s definition of development because of its effects on humans and their freedoms in life. Poverty can be described as living conditions that are detrimental to health, economic development, and comfort. This restricts human freedoms because it can lead to higher infant mortality, shorter life spans, lower literacy, environmental degradation, and loss of biodiversity. In order to increase human development, poverty must be eradicated so that all can enjoy the freedoms of life. Luckily, the introduction of data science and its applications in science and research have allowed us to start to more effectively combat poverty.
Data science as mentioned above is a fairly new method of research and science. It involves the use of different data such as satellite data or mobile data to measure and collect information on different subjects such as poverty. There are already methods for combating poverty such as anti-poverty agencies, but the use of data science has been the most effective so far in mapping poverty across the world. Although data science is being used globally, this literature review specifically targets the region of Southeast Asia where the chance of poverty is extremely high.
Southeast Asia has many developing countries, but a major factor in their growth is agricultural research investment. Studies have shown that under investment in agricultural research across Southeast Asia has contributed to the high poverty rate. As the region is still developing a boost in the agricultural systems may be an important step forward in combating poverty. In order to identify and reduce poverty rates, researchers have used different sources of data to map and analyze the region. The data methods used in the research of investment in agriculture are spatial data mapping and patterns in consumption, production, and impact.
The purpose of this research is to recognize the use of data patterns and of spatial data mapping in combating poverty in Southeast Asia. But it is also to improve upon the use of these data methods to more effectively reduce poverty and drive human development further. This research specifically targets agricultural research, and how it can be used to reduce poverty in the Southeast Asia region.
Agriculture is extremely important to Southeast Asia as sixteen percent of its Gross Domestic product comes from agriculture. Unfortunately, Southeast Asia also holds one of the world’s lowest agricultural research intensity ratios. There is evidence of agricultural growth being the driver of development in Southeast Asia. But as a result, this indicates that there is under investment in the region. Although all signs suggest that there should be increased investment, there is no clear view on if the region will follow. If the region increased their investments, the environment and those in poverty in the region would greatly benefit from it.
Southeast Asia is growing in agriculture, but the region has not decided on commercialization or self-sufficiency. As a result many studies were conducted to come to a conclusion on how to set up the agricultural investment in the region. One particular study from David A. Raitzer and Mywish K. Maredia was used to combat poverty and help the poor. It involved using many different data methods together to create an approach that helps those in poverty.
A data method used by many researchers in studies were data patterns. Data Patterns are facts or characteristics that are recognizable in datasets. Through data collection, a researcher can find a pattern and draw conclusions from it. Data patterns can provide many different results and can be found in all different kinds of data collections. A couple examples of data patterns are productivity and consumption patterns. Productivity and consumption patterns can be inputted into equations to produce more data projections.
In Southeast Asia, some data patterns that are examined are the productivity and consumption patterns of agricultural products. In the study of productivity data in Southeast Asia, it was found that the rice production was the dominant product. Some other products that were highly produced were oil palm or Elais guineensis, aquaculture, pork, poultry, rubber, vegetables, and fruit. The data was recorded over the course of many years and then were used to predict production values in the future. As a result the data showed the rise of some production values but the fall of others.
As mentioned before, data can be inputted into different equations to produce more data patterns. One study projected economic surpluses from the data, this included aggregate surplus, consumer surplus, and producer surplus. Through the examination of the data, overall economic benefits could be identified for the region’s agricultural investment. For Southeast Asia the productivity of rice yielded the greatest economic benefits. It was found that exports were the most important in improving the economy of Southeast Asia.
Through the use of data patterns, it was projected that an investment in agriculture and specifically exports of Southeast Asia would greatly benefit the economy. An economic boost would help reduce the poverty levels in the region. Thus helping combat the problem of poverty, which would in turn help spur more human development.
Spatial data or geospatial data is data recorded relating to a specific location through geography whether it be Earth, the Moon, or etc. Spatial data can come in different types such as vector data or raster data. Spatial data is commonly analyzed using geographic information system or GIS.
In the study of poverty in Southeast Asia, spatial data was recorded and used to estimate production patterns of the poor in order to create a map. A map of the proportion of the population living on less than a dollar and twenty five cents per day was created. Another map showing the agricultural systems with the highest production values was created through the contributions of the International Food Policy Research Institute (IFPRI), the Center for International Earth Science Information Network (CIESIN), the International Center for Tropical Agriculture (CIAT), and the World Bank. It even included the use of raster grid spatial data.
The data was able to be used to also create a value of production based on the weight of poverty. As a result the surplus data was able to be changed to reflect those living in poverty too. The data indicated that rice has the largest portion of benefits to poor producers. It can also be concluded that the data is an understatement because the data did not include a large number of those in poverty.
Consumer prices are extremely important to those living in poverty as they do not have the income to provide for themselves and their families. It was found that sixty four percent of income from the poor in Southeast Asia is used on food. Projections for a fifteen percent decrease in food prices were found to be equivalent to a ten percent increase in come for the poor. It can be concluded that food expenditures are the most important for poor consumers.
Consumer Data Patterns are sometimes recorded from historical trends and are then used to project consumption in the future. This was used to project food expenditure ratios of those in poverty in Southeast Asia. It is found that rice is the largest food expenditure among the region. The data found from the poor consumers can also be used to show the effects of poverty. One study used the consumer data pattern of the poor to compare it to the poor in poverty. It was concluded that once again rice dominated the expected benefits.
As a result of agricultural research, researchers can find data for many other factors contributing to poverty such as environmental degradation. Some leading causes of environmental degradation can be examined through agriculture research such as water depletion, the emission of greenhouse gases, and effects on the landscape of the region.
Patterns that can be concluded from agricultural research is that if there were an increase in agricultural investment, Southeast Asia would greatly benefit and grow as a region. The most efficient and beneficial crop is rice. Following rice would be palm oil, aquaculture, pork, poultry, and etc. The investment in agricultural research to increase the productivity of crops would greatly help the developing region. Those in poverty would face great benefits from investment especially in the rice crop.
Poverty is a major factor in inhibiting the expansion of human development today. As Amartya Sen stated, human development is based on the process of expanding freedoms of humans. It is found that nearly half the world’s population lives on less that two dollars and fifty cents a day. In today’s world poverty is a major factor in restricting this because those in poverty are not able to meet the basic needs of life.
From the research done, it is found that poverty is an extremely complex issue. There are many factors that contribute to poverty, and it is widespread across the globe. It has major economic features, but also holds social and environmental features. An example of an economic feature of poverty is how a person is affected because of their economic situation. If an individual has an extremely low income, they face a greater chance of being affected by poverty. Poverty contains social features because the social class system can affect an individual’s chance of poverty. Such as those born in lower social classes, they do not have the same opportunities as those in higher social classes, and they face a higher risk of falling into poverty.
Poverty is somewhat predictable and recordable based on research in many different studies. As seen in data patterns and spatial data, the poverty population is somewhat able to be recorded, but at the same time there are still issues in recording poverty as data methods are still fairly new. As data methods are used more over time and technology progresses, poverty will be able to be recorded accurately and precisely.
This literature review discusses the collection of data, the computation of the collected data into data patterns and maps, and the application of the patterns in maps in agricultural investment in Southeast Asia to reduce poverty levels in the region. It was found that agricultural research investment into the production of rice would bring the greatest benefit in Southeast Asia. This was found to be an effective way to project data to find a way to help those that are poor and in poverty in the region through agricultural research investment.
Although results were found to greatly benefit the region, there is still little action being taken in applying the findings. As a result poverty in Southeast Asia is inconsistently being reduced across the region. The findings of the research also had some pitfalls due to the unpredictability of the economy, the findings were only projections and could possibly have some flaws, and the data was skewed because there are many of those in poverty that were not recorded. Therefore the results are based on higher potential and do not accurately describe the whole population of people facing poverty. Another pitfall is that within the region of Southeast Asia many countries act independently and therefore the findings are a general estimate for the whole region.
Improvements to this research can be made through more concentrated research within the region of Southeast Asia. Each country’s agricultural investment can be more closely researched in order to come to more accurate findings. As a result of more specific data collection and more accurate data collection, agricultural investment can be projected to reduce the poverty in Southeast Asia. Then the researcher can ask, what factors contributing to poverty in Southeast Asia can be more easily recorded using data methods such as spatial data and data patterns?